ggplot(data=Orange, aes(x=Orange$age, y=Orange$circumference)) + geom_point()

ggplot(data=Orange %>% filter(Tree==1), aes(x=age, y=circumference)) + geom_point()

ggplot(data=Orange %>% filter(Tree==1), aes(x=age, y=circumference)) + geom_line()

ggplot(data=Orange, aes(x=Tree, y=circumference)) + geom_boxplot() 

ggplot(data=Orange, aes(x=Tree, y=circumference)) + geom_boxplot() + geom_point()

ggplot(data=Orange, aes(x=circumference)) + geom_histogram() 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=Orange, aes(x=circumference)) + geom_histogram(binwidth=50)

ggplot(data=Orange, aes(x=circumference)) + geom_histogram(binwidth=50) +
  geom_vline(xintercept=median(Orange$circumference), col="red")

ggplot(data=Orange, aes(x=circumference)) + geom_density() 

ggplot(data=Orange, aes(x=circumference, y=age)) + geom_point() + facet_grid(~Tree) 

levels(Orange$Tree) <- sort(levels(Orange$Tree))
ggplot(data=Orange, aes(x=circumference, y=age)) + geom_point() +
  geom_line(col="red") + facet_wrap(~Tree, ncol=2) 

ggplot(data=Orange, aes(x=circumference, y=age, col=Tree))  + geom_line()

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

ggplot(data=iris, aes(x=Petal.Length, fill=Species))  + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=iris, aes(x=Petal.Length, color=Species))  + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=iris, aes(x=Petal.Length, fill=Species))  + geom_histogram(color="black") 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

require(ggplot2movies)

## Loading required package: ggplot2movies

head(movies)

## # A tibble: 6 x 24
##   title  year length budget rating votes    r1    r2    r3    r4    r5
##   <chr> <int>  <int>  <int>  <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 $      1971    121     NA    6.4   348   4.5   4.5   4.5   4.5  14.5
## 2 $100…  1939     71     NA    6      20   0    14.5   4.5  24.5  14.5
## 3 $21 …  1941      7     NA    8.2     5   0     0     0     0     0  
## 4 $40,…  1996     70     NA    8.2     6  14.5   0     0     0     0  
## 5 $50,…  1975     71     NA    3.4    17  24.5   4.5   0    14.5  14.5
## 6 $pent  2000     91     NA    4.3    45   4.5   4.5   4.5  14.5  14.5
## # ... with 13 more variables: r6 <dbl>, r7 <dbl>, r8 <dbl>, r9 <dbl>,
## #   r10 <dbl>, mpaa <chr>, Action <int>, Animation <int>, Comedy <int>,
## #   Drama <int>, Documentary <int>, Romance <int>, Short <int>

tipi = names(movies)[18:23]
seznam = list()
for (i in 1:length(tipi)) {
    tip = tipi[[i]]
    seznam[[i]] <- movies %>% 
        filter_(paste(tip, "==", 1)) %>% 
        select(Budget=budget, Short, Year=year) %>%
        mutate(Type=tip)
}
myMovies <- do.call(rbind, seznam)

ggplot(data=myMovies, aes(x=Type, fill=Type))  + geom_bar() 

ggplot(data=myMovies, aes(x=Type, fill=factor(Short)))  + geom_bar() 

ggplot(data=myMovies, aes(x=Type, fill=factor(Short)))  + geom_bar(position="stack") 

ggplot(data=myMovies, aes(x=Type, fill=factor(Short)))  + geom_bar(position="dodge") 

ggplot(data=myMovies, aes(x=Type, y=Budget))  + geom_boxplot() 

ggplot(data=myMovies, aes(x=Type, y=Budget))  + geom_boxplot() + scale_y_log10()

ggplot(data=myMovies, aes(x=Type, y=Budget))  + geom_boxplot() +
  scale_y_log10() + geom_point()

ggplot(data=myMovies, aes(x=Type, y=Budget))  + geom_jitter() +
  geom_boxplot(alpha=I(0.6)) + scale_y_log10() 

ggplot(data=myMovies, aes(x=Type, y=Budget))  + geom_jitter() + geom_boxplot(alpha=I(0.6)) + scale_y_log10() 

ggplot(data=myMovies, aes(x=Year, y=Type, size=Budget)) + geom_point()

head(ToothGrowth)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

ggplot(data=ToothGrowth, aes(x=dose, y=len))  + geom_point() 

ggplot(data=ToothGrowth, aes(x=dose, y=len, col=supp))  + geom_point() 

ggplot(data=ToothGrowth, aes(x=dose, y=len, col=supp))  + geom_point() +
  facet_grid(.~supp) 

ggplot(data=ToothGrowth, aes(x=dose, y=len, col=supp))  + geom_point() +
  facet_grid(.~supp) + stat_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data=ToothGrowth, aes(x=dose, y=len, col=supp))  + geom_point() + 
  facet_grid(.~supp) + stat_smooth(method="lm")

head(economics)

## # A tibble: 6 x 6
##   date         pce    pop psavert uempmed unemploy
##   <date>     <dbl>  <int>   <dbl>   <dbl>    <int>
## 1 1967-07-01  507. 198712    12.5     4.5     2944
## 2 1967-08-01  510. 198911    12.5     4.7     2945
## 3 1967-09-01  516. 199113    11.7     4.6     2958
## 4 1967-10-01  513. 199311    12.5     4.9     3143
## 5 1967-11-01  518. 199498    12.5     4.7     3066
## 6 1967-12-01  526. 199657    12.1     4.8     3018

ggplot(data=economics, aes(x=date, y=unemploy)) + geom_line()

myMovies$RoundYear <- signif(myMovies$Year, digits = 3)
ggplot(data=myMovies,aes(Budget)) + geom_histogram(binwith=1) + 
  facet_grid(.~Type)+ scale_x_log10()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=myMovies,aes(Budget)) + geom_histogram(binwith=1) + 
  facet_grid(Type~.) + scale_x_log10()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=myMovies,aes(Budget)) + geom_histogram(binwith=1) + facet_grid(RoundYear~Type) + scale_x_log10()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=subset(myMovies, RoundYear>1980), aes(Budget)) + 
  geom_histogram(binwith=1) + facet_grid(.~Type+RoundYear) + 
  scale_x_log10()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.